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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

System Study: High-Pressure Safety Injection 1998-2024

This report presents an unreliability evaluation of the high-pressure safety injection system (HPSI) at 62 U.S. commercial operating nuclear reactors. New Standardized Plant Analysis Risk (SPAR) models with the most recent SPAR parameter update results were used in this report. Demand, run hour, and failure data from calendar years 1998 to 2024 for selected components were obtained from the Institute of Nuclear Power Operations Industry Reporting and Information System. The unreliability results are trended for the most recent 10-year period and yearly estimates for system unreliability are provided for the entire active period. Statistically significant decreasing trends were identified in both the HPSI system start-only unreliability and 24-hour mission unreliability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Farm Practice Typologies as a Strategy for Management-Relevant Land Use and Land Cover Mapping in the Great Lakes Region (Version 1) [Dataset]

Dataset overview and development This dataset provides spatially explicit agricultural land-use and land-management typologies developed for the Great Lakes Region (GLR) at the farm-parcel level. The typologies were designed to characterize not only the land-use and land-cover (LULC) associated with individual agricultural farm parcels, but also the land-management practices (LMPs), including irrigation, tile drainage, and conservation easements, occurring within those parcels and how these characteristics change through time. The dataset contains four related typology products: Annual integrated typology – describes the combined LULC and land-management characteristics for each farm parcel for individual years. LULC transition typology – describes the temporal pattern of LULC change for each farm parcel across the study period (2008-2023). LMP trend typology – describes the temporal pattern in the occurrence of LMPs for each farm parcel across the study period. Multi-year integrated typology – combines the LULC transition typology and LMP trend typology to provide an integrated characterization of long-term land-use and management patterns. Purpose of the dataset The purpose of these products is to provide a management-relevant integrated and consistent framework for evaluating the spatial and temporal organization of agricultural landscapes across the GLR. The resulting typologies can: support landscape-scale environmental and land-use analysis; provide spatial information relevant to land-management strategies, conservation planning, policy development, and program evaluation; characterize spatial patterns of agricultural land use and management; examine changes in agricultural landscapes through time; and identify persistent, transitional, and changing agricultural systems. Please refer to the README file provided in Files for more details.

Agriculture↗
DOE OSTI2021

Fall 2021 Solar Industry Update

Each quarter, the National Renewable Energy Laboratory (NREL) conducts the Quarterly Solar Industry Update, a presentation of technical trends within the solar industry, to the solar office staff. Each presentation focuses on global and U.S. supply and demand, module and system price, investment trends and business models, and updates on U.S. government programs supporting the solar industry.

component↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Mapping the Binary Covalent Alloy Space to Pursue Superior Nitrogen Reduction Reaction Catalysts

The electrochemical nitrogen reduction reaction (NRR) has the potential to decarbonize industrial ammonia production. However, NRR has poor activity and selectivity versus the competing hydrogen evolution reaction for catalysts that adhere to scaling relations. Overcoming the limitations imposed by scaling relations requires more complex catalyst materials, however, evaluating materials beyond simple metal systems is a large combinatorial problem that requires an improved understanding of the electrocatalyst surface to rationally guide the discovery of superior catalysts. The study uses grand canonical density functional theory to uncover NRR trends on a large and disparate set of binary covalent alloys (BCA) with variable compositions and active-site geometries. The studied BCAs generally follow scaling relations, albeit with larger variance and several systems that significantly break scaling. BCAs with early- to mid-transition metals tend to lie near the volcano peak and activate the N 2 triple bond via a side-on binding configuration. Trends in the BCA space cannot be readily predicted using simple electronic descriptors, which is ascribed to the large geometric variability of the BCA surfaces. Furthermore, it is anticipated that these findings will provide a foundation for the rational design of superior NRR electrocatalysts with increasing material complexity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Abstract Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb 24 O 72 H 9 ] 15− , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14− , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

Rahman, Tasnim↗
DOE OSTI2023

Hydrogen Evolution on Electrode‐Supported Pt n Clusters: Ensemble of Hydride States Governs the Size Dependent Reactivity

Abstract We report the size‐dependent activity and stability of supported Pt 1,4,7,8 for electrocatalytic hydrogen evolution reaction, and show that clusters outperform polycrystalline Pt in activity, with size‐dependent stability. To understand the size effects, we use DFT calculations to study the structural fluxionality under varying potentials. We show that the clusters can reshape under H coverage and populate an ensemble of states with diverse stoichiometry, structure, and thus reactivity. Both experiment and theory suggest that electrocatalytic species are hydridic states of the clusters (≈2 H/Pt). An ensemble‐based kinetic model reproduces the experimental activity trend and reveals the role of metastable states. The stability trend is rationalized by chemical bonding analysis. Our joint study demonstrates the potential‐ and adsorbate‐coverage‐dependent fluxionality of subnano clusters of different sizes and offers a systematic modeling strategy to tackle the complexities.

Zhang, Zisheng↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali-counterions can inform natural and synthetic aqueous processes. Here in the study of POMs ([Nb 24 O 72 H 9 ] 15- , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ-profile) and use an artificial neural network (ANN) to predict all three described alkali-anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14- , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hydrogen Evolution on Electrode–Supported Ptn Clusters: Ensemble of Hydride States Governs the Size Dependent Reactivity

We report the size-dependent activity and stability of supported Pt1,4,7,8 for electrocatalytic hydrogen evolution reaction, and show that clusters outperform polycrystalline Pt in activity, with sizedependent stability. To understand the size effects, we use DFT calculations to study the structural fluxionality under varying potentials. We show that the clusters can reshape under H coverage and populate an ensemble of states with diverse stoichiometry, structure, and thus reactivity. Both experiment and theory suggest that electrocatalytic species are hydridic states of the clusters (~2 H/Pt). An ensemble-based kinetic model reproduces the experimental activity trend and reveals the role of metastable states. Furthermore, the stability trend is rationalized by chemical bonding analysis. Our joint study demonstrates the potential- and adsorbate-coverage-dependent fluxionality of subnano clusters of different sizes and offers a systematic modeling strategy to tackle the complexities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Au x Pd (300‐ x ) Alloy Nanoparticles for the Oxygen Reduction Reaction in Alkaline Media

Abstract Au x Pd (300‐ x ) nanoalloys were prepared using a dendrimer‐templating method and their electrocatalytic efficiencies towards the oxygen reduction reaction (ORR) were analyzed in alkaline media. The composition‐dependent PdO x reduction potentials of the Au x Pd (300‐ x ) nanoalloys were correlated to the oxygen‐binding (O‐binding) energy of the catalysts. Multiple PdO x reduction peaks were present for the Au 150 Pd 150 and Au 50 Pd 250 catalysts. These peaks were assigned to the alloyed and bulk‐like Pd active sites on the alloyed surface. The results described here are significant because modulation of the O‐binding energies improved the ORR activity on the nanoalloys. Specifically, a linear trend in O‐binding energies of Au x Pd (300‐ x ) dendrimers (DENs) as a function of Pd composition resulted in a volcano‐shaped trend in ORR activity. The optimal O‐binding energy of Au 50 Pd 250 resulted in an approximate 89 mV shift in PdO x reduction relative to Pd 300 and a shift of approximately 76 mV in ORR peak potential.

Trindell, Jamie A.↗
DOE OSTI2021

Probing the Effects of Acid Electrolyte Anions on Electrocatalyst Activity and Selectivity for the Oxygen Reduction Reaction

The local microenvironment at the electrode-electrolyte interface plays an important role in electrocatalytic performance. Herein, we investigate the effect of acid electrolyte anion identity on the oxygen reduction reaction (ORR) activity and selectivity of smooth Ag and Pd catalyst thin films. Cyclic voltammetry in perchloric, nitric, sulfuric, phosphoric, hydrochloric, and hydrobromic acid, at pH1, reveals that Ag ORR activity trends as follows: HClO 4 >HNO 3 >H 2 SO 4 >H 3 PO 4 >HCl>>HBr, while Pd ORR activity trends as: HClO 4 >H 2 SO 4 >HNO 3 >H 3 PO 4 >HCl>>HBr. Moreover, rotating-ring-disk-electrode selectivity measurements demonstrate enhanced 4 e - selectivity on both Ag and Pd, by up to 35% H 2 O 2 and 10% H 2 O 2 respectively, in HNO 3 compared to in HClO 4 . Relating physics-based modeling and experimental results, we postulate that ORR performance depends greatly on anion-related phenomena in the double layer, for instance competitive adsorption and non-covalent interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular Simulations of the Chain Length Dependent Adsorption of C7‐C14 n ‐Alkanes in ZIF‐8

Recent experiments show that the diffusivities of C8 and C10 n-alkanes in ZIF-8 are higher than those of C7 and C9, respectively. We investigated this unusual ‘odd-even' effect by simulating the adsorption of C7-C14 n-alkanes in ZIF-8 using hybrid Monte Carlo molecular simulations. The resultant adsorption isotherms, guest-host energies, isosteric heats, and chain length distributions are analyzed for trends among the n-alkanes. ZIF-8 cages filled with n-alkanes are characterized using a combination of image processing and data science to quantify the differences in packing that occur with chain length. In conclusion, results indicate that packing changes drastically from C7 to C8 and from C9 to C10, which is consistent with the diffusion trends previously reported in experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Single-atom automobile exhaust catalysts

Single-atom catalysts (SACs) with isolated metal centers are attracting great research interest because of their maximized metal utilization rates and unique structures. In the last decade, significant efforts have been made to design highly efficient and costeffective SACs with applications in automobile exhaust aftertreatment. SACs have not only demonstrated their high potential for improving the catalytic performance of current automobile exhaust catalysts but also provided an ideal platform for understanding the origins of catalyst reactivity. In this review, we summarize the general strategies for promoting the reactivity of metal SACs while maintaining their thermal stability during exhaust-abatement-related reactions. Highlights are provided on well-performed SACs for CO oxidation, unburnt hydrocarbon (HC) oxidation, and the selective catalytic reduction of NOx. The reaction mechanism and structure– performance relationship of SAC during these reactions are also discussed. Finally, perspectives are given on the trending research fields for designing more efficient single-atom automobile exhaust catalysts in the future.

Lu, Yubing↗
DOE OSTI2024

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

Abstract Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.

Chemistry↗
DOE OSTI2021

Alcohol consumption and breast cancer risk in Japan: A pooled analysis of eight population‐based cohort studies

Abstract Although alcohol consumption is reported to increase the incidence of breast cancer in European studies, evidence for an association between alcohol and breast cancer in Asian populations is insufficient. We conducted a pooled analysis of eight large‐scale population‐based prospective cohort studies in Japan to evaluate the association between alcohol (both frequency and amount) and breast cancer risk with categorization by menopausal status at baseline and at diagnosis. Estimated hazard ratios (HR) and 95% confidence intervals were calculated in the individual cohorts and combined using random‐effects models. Among 158 164 subjects with 2 369 252 person‐years of follow‐up, 2208 breast cancer cases were newly diagnosed. Alcohol consumption had a significant association with a higher risk of breast cancer in both women who were premenopausal at baseline (regular drinker compared to nondrinker: HR 1.37, 1.04‐1.81, ≥23 g/d compared to 0 g/d: HR 1.74, 1.25‐2.43, P for trend per frequency category: P = .017) and those who were premenopausal at diagnosis (≥23 g/d compared to 0 g/d: HR 1.89, 1.04‐3.43, P for trend per frequency category: P = .032). In contrast, no significant association was seen in women who were postmenopausal at baseline or at diagnosis, despite a substantial number of subjects and long follow‐up period. Our results revealed that frequent and high alcohol consumption are both risk factors for Asian premenopausal breast cancer, similarly to previous studies in Western countries. The lack of a clear association in postmenopausal women in our study warrants larger investigation in Asia.

Iwase, Madoka↗
DOE OSTI2020

Identifying the mechanisms of cation inhibition of phenol oxidation by acid birnessite

Abstract Many phenolic compounds found as contaminants in natural waters are susceptible to oxidation by manganese oxides. However, there is often variability between oxidation rates reported in pristine matrices and studies using more environmentally relevant conditions. For example, the presence of cations generally results in slower phenolic oxidation rates. However, the underlying mechanism of cation interference is not well understood. In this study, cation co‐solutes inhibit the transformation of four target phenols (bisphenol A, estrone, p ‐cresol, and triclosan) by acid birnessite. Oxidation rates for these compounds by acid birnessite follow the same trend (Na + > K + > Mg 2+ > Ca 2+ ) when cations are present as co‐solutes. We further demonstrate that the same trend applies to these cations when they are absent from solution but pre‐exchanged with the mineral. We analyze valence state, surface area, crystallinity, and zeta potential to characterize changes in oxide structure. The findings of this study show that pre‐exchanged cations have a large effect on birnessite reactivity even in the absence of cation co‐solutes, indicating that the inhibition of phenolic compound oxidation is not due to competition for surface sites, as previously suggested. Instead, the reaction inhibition is attributed to changes in aggregation and the mineral microstructure.

Balgooyen, Sarah↗
DOE OSTI2020

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

Since the first detailed compositional analysis of dissolved organic matter (DOM) in 1995, high resolution mass spectrometry (HRMS) has become a vital tool for DOM characterization. While the upward trend in HRMS for molecular level analysis of DOM continues, so do the challenges of data comparison and interpretation between laboratories operating instruments of differing performance and user operating conditions. It is therefore essential that the community establishes whether data and trends can be compared robustly between research groups. To this end, 4 identically prepared DOM samples were each studied by 16 laboratories, representing 17 commercially-purchased instruments located across 8 countries, using positive- and negative-mode electrospray ionization (ESI) HRMS analysis. Despite the widely reported sensitivity of ESI experiments to sample matrix, preparation, ion source operation and instrument tuning parameters, the instruments used in this study showed relatively low variability between results for the same samples, particularly H/C and AI metrics. The variation in O/C and m/z metrics was relatively higher, although z-score graphs and %BCD demonstrated that the relative difference in general chemical composition between samples was the same for each instrument included in the study. As such, these metrics can be used for fingerprinting of DOM samples and as a benchmark for quality control in participating laboratories, with future strides in environmental science building upon these efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗